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Particles or
fluids? It is tempting to expect that
complex
phenomena must also
have complicated causes.
Or more specifically, that complex properties of a community would be
no less difficult to describe than complex properties of the
individuals within. It has now become clear that complex traits of
individuals (e.g. intelligence in humans or yield of crops) are often
highly polygenic; i.e. variation in these traits is driven by many
genetic variants that each contribute small effects. Understandably,
their inheritance seemed so complicated, that early models assumed
the inherited principles to “mix and flow” like a fluid. One of
our celebrated science heroes, the famous monk Gregor Mendel with his
strong interest in horticulture, demonstrated otherwise: through the
study of peculiar traits in peas, comprehensive crossing schemes, and
combinatorial logic he inferred that inheritance resembles the mixing
and re-arrangement of “particles” rather than that of “fluids”1⁠.
He had just outlined an astonishingly simple model of inheritance
still used today.

From cells
to individuals to communities. Over the course of
my career, I had gradually moved from studying small things (e.g.
single cells) to studying bigger things (e.g. organs, individuals) by
the use of molecular genetics. When joining an inspiring new
environment after my PhD, the community ecology group led by Bernhard
Schmid (University of Zurich), I came across an intriguing phenomenon
at an even higher level of biological organization – biodiversity
effects. These are emergent properties of groups or communities, in
which diversity more often than not increases group productivity and
functioning (Figure 1). I was immediately hooked.

The
long-standing puzzle of biodiversity. Biodiversity
effects have been studied extensively – I still get lost in the
vast literature! – with evidence of effects of diversity reported
at a range of organizational levels (e.g. genotypes, species,
communities) and on a range of community functions (e.g. nutrient
retention, productivity). While the basic hypotheses of positive
interactions among functionally different species can in fact be
traced back to Darwin2⁠, it was only in the early
1990ies that this idea attracted a renewed interest3⁠
and was hugely popularized by the work of David Tilman and colleagues
at the now-famous Cedar Creek grassland experiments4,5.
And yet, surprisingly little is currently known about the mechanisms
that underlie these effects.

I decided to pick up
this challenge – and got the necessary and extremely valuable
support of my colleague and expert in the field, Pascal Niklaus
(Figure 2). Together, we decided to use genetics to examine whether
the mechanisms behind diversity effects are indeed as complicated as
one could naively expect (i.e caused by polygenic differences between
individuals) or whether they are more simple (“Mendelian”).
Looking back, it really was one of these projects with uncertain
outcomes and high risk of failure - but very soon, the first results
would blow our minds!

Figure 2: Left and
center: Greenhouse and simple model plant communities that were used
in this work. Communities consisted of four plants, and either one
(monoculture) or two (mixture) genotypes, and above-ground
productivity was mainly the property of interest. For this and a yet
unpublished project on biodiversity effects, many, many plants were
grown. Many! Right: The two authors in their natural habitat.

A most
unusual genetic map. For our work, we
used very simple model plant communities grown in a greenhouse, and
focused on genotypic (not
species) diversity
effects on above-ground productivity in Arabidopsis, the
genetic plant model of choice. In order to genetically dissect the
effects seen in a specific
combination of Arabidopsis genotypes, we utilized recombinant
lines derived from a cross between these, and developed our first
quantitative trait locus (QTL) map of a biodiversity effect. To our
amazement, the map turned out extremely simple: it pointed to a
single major effect locus on chromosome four (Figure 3). In essence,
plant groups containing individuals that were genetically different
at this one QTL were more productive than groups containing
individuals that were not different. I was amazed, and yet
uneasy. Was this just too good to be true? Starting from our simple
genetic map, much more work would be needed until our doubts were
fully dispelled. And this in times of dwindling funds and with
unemployment constantly looming around the corner. The experiences
gained were exciting and stressful at the same time. Reluctantly, I
have to admit that during the analysis of a key experiment I even
dropped a few tears in awe and relief. I am just glad there were no
witnesses.

Figure 3: A genetic
map of diversity effects on aboveground productivity. A major effect
locus is visible on lower arm of chromosome four, a region in which
the genetic difference between individuals leads to higher than
expected group productivity. LOD:
logarithm of the odd. Ticks
above the x-axis represent molecular
markers. Adapted from Figure 2b of our paper.

A small
difference can have a large impact. By tapping
deeper into the genetic toolbox and through multiple, independent
means, we then demonstrated that a single discrete genetic element
can drive a large part of the studied genotypic diversity effect: we
fine-mapped this effect to a rather narrow chromosomal interval and
showed that diversity in this region acts independently of the rest
of the genome (Figure 4).

Figure 4: A
reductionist genetic model of genotypic diversity effects. Left: In
our work, we found that groups of individuals with near-identical
genomes but differing at a single chromosomal region were more
productive than groups with of individuals with identical genomes.
Right: This suggests a simple model, whereby within such plant
communities, two types of “particles” (i.e. alleles in a
homozygous state) synergistically interact with each other.

As we went on, we
also observed further twists and complications, e.g. that the
diversity effect at the identified locus strongly depended on soil
type (just to make things more complicated). The most intriguing
finding, however, was that strong interactions between individuals
carrying different alleles not only occured when they grew
simultaneously in a community! When we re-used the soil conditioned
by the plant communities from our mapping experiment, we measured a
persistent legacy of allelic diversity on the growth of a new
generation of plants – mediated through (yet) unknown soil
factor(s) (Figure 4). In summary, these are seemingly very complex
community properties – which our map and experiments suggest to
have simple genetic causes.

Figure 5: Strong
interactions between alleles at the identified locus are not
restricted to simultaneously grown individuals. As we re-used soil
from the communities used for genetic mapping for a next generation
of plants, the growth of indicator plants (“phytometers”) were
significantly influenced by allelic diversity in the previous
generation. Adapted from Figure 1d of our paper.

Particles, not
fluids, underlie complex community properties. Many
people I have presented our results to were, though intrigued, also a
bit disappointed. Our model remains rather abstract. Many would have
preferred references to specific names of genes or chemicals
involved. I am happy to say that we are making rapid progress in that
direction, and knowing said details will likely enhance our
understanding of biodiversity effects. Yet my own fascination about
genetics has always been fuelled by papers that highlight the amazing
capacity of the human mind for abstraction, through which simple,
macroscopic observations are turned into a deeper understanding of
nature. My favourite examples of this are, quite naturally, the
inferrence of a particulate mode of inheritance by counting peas5⁠;
but also predicting the triplet nature of the genetic code by couting
“dots” (phage plaques) on a bacterial plate6⁠.
Humbled by - but hopefully in the best tradition of - such work we
conclude that the complex community properties we studied are driven
by “particles, not fluids”.

Acknowledgements.
This work was enabled by the very generous support by Prof.
Bernhard Schmid and Prof. Ueli Grossniklaus, the helping hands of
Matthias Philipp, Matthias Furler, Dorde Topalovic and Enrica De
Luca, and funds from the Swiss National Science Foundation, the
University of Zurich and the European Comission. Finally, I would not
have finished it without the continous support of my wife, Chloé
Amanda.

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